Océane Cassan

The paper develops a machine learning method to identify cis-regulatory element profiles and their sequence features during neuronal differentiation using single-cell ATAC-seq and RNA-seq data.

The paper optimizes data integration for GRN inference in Arabidopsis thaliana by introducing DIOgene, which improves prediction accuracy and identifies gene-specific integration intensities.

The paper introduces TFcoop, a method to infer TF combinations involved in target TF binding, and shows it outperforms PWM methods while revealing cell-type-specific and promoter-class-specific TF interactions.